The maintenance of road pavements is an essential task to prevent major deterioration and to reduce accident rates. In this task, the detection and classification of different types of cracks on the roads is usually considered. However, in most cases, these tasks are not fully automated and they need to be supervised by an expert to make repair decisions. This work focuses on the automatic classification of the most common types of cracks: longitudinal cracks, transverse cracks, and alligator cracks. Our proposal combines, first, computer vision techniques for crack segmentation and second, an ensemble model (composed of different rule‐based algorithms) for the classification. This approach achieves an average precision and recall values greater than 94% for three analyzed data sets improving the results in comparison to other approaches.

中文翻译：

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集成模型在路面裂化分类中的优势

维护路面是防止重大恶化和降低事故率的重要任务。在此任务中，通常考虑对道路上不同类型的裂缝进行检测和分类。但是，在大多数情况下，这些任务不是完全自动化的，需要由专家进行监督才能做出维修决策。这项工作着重于对最常见的裂纹类型进行自动分类：纵向裂纹，横向裂纹和鳄鱼形裂纹。我们的建议首先结合了用于分割的计算机视觉技术，其次结合了用于分类的集成模型（由不同的基于规则的算法组成）。与其他方法相比，该方法对三个分析数据集的平均精度和召回率均大于94％，从而改善了结果。